Table 1 Overview of included studies

Tool development

 Hughes et al. [24], 2020

UK

Consensus event:

i) Literature review, ii) consensus meeting

iii) focus groups and interviews

S: Care home:

i) consensus meeting (n = 4 experts)

ii) focus groups (n = 6 care home staff & n = 6 resident families) and interviews (n = 8 GPs)

Algorithm adapted from Loeb et al. [40]

Algorithm

UTI, respiratory tract, skin & soft tissue

One or more new/worsening symptoms: suspected fever, change in behaviour, reduced mobility, loss of appetite and/or the typical infection symptoms.

 Van Buul et al. [25], 2018

USA, Netherlands, Canada, Sweden and Australia

Delphi consensus procedure

i) Expert panel

ii) Delphi rounds ×4

S: Nursing home

i) Expert panel (n = 15 old care physicians)

ii) Response rates to the 4 Delphi questionnaires were

100, 88, 94, and 88%, respectively (same sample as expert panel)

Decision tool for the empiric treatment of suspected UTI in frail older adults

Algorithm

UTI

• No indwelling catheter: recent onset of dysuria, urgency, frequency, incontinence, visible urethral purulence, change in urine colour, macroscopic haematuria, pain, mental status change, general lack of well-being, decreased intake, diarrhoea, nausea, vomiting, malaise, fatigue, weakness, dizziness, syncope, decreased functional status.

• Indwelling catheter: no other infectious focus plus at least: fever (> 24 h), rigors/shaking chills, clear-cut delirium (after excluding urinary retention as a possible cause)

Tool development and reliability

 Matsusaka et al. [26], 2018

Japan

Retrospective case series review

S: Hospital

n = 102 bedridden patients receiving oral care

A bedridden patient pneumonia risk (BPPR) score

Checklist

Pneumonia

• Albumin < 3.5 g/dL or/and urine bacteria were the two only risk factors associated independently with pneumonia. Not: age, sex, BMI, WBC, Lymphocyte, CRP, Hb, Iron, TP, TC, BUN, Creatinine, CPK, or Low uric acid.

• Total BPPR score is 0,1, or2 (low-moderate and high risk) according to absence or presence of the two risk factors.

Tool development and validation

 Rawson et al. [27], 2019

UK

Development & cross validation of supervised machine learning

S: Hospital (n = 3)

n = 104 patients diagnosed with infection within 72 hrs of admission

Supervised machine learning (SML) algorithm for diagnosing bacterial infection

Algorithm

Bacterial

• Microbiology records and six available blood parameters (CRP, WCC, bilirubin, creatinine, ALT and ALP).

• Sensitivity and specificity: the infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P = 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91).

 García-Tello et al. [28], 2018

Spain

Retrospective cohort study

S: Hospital

n = 1524 patients with UTI i) development cohort n = 1067 (70%)

ii) validation cohort: n = 457 (30%)

Nomogram to predict the probability of infection by extended-spectrum beta-lactamase (ESBL)-producing microorganisms.

Nomogram model

UTI

• Age, male gender, nursing home residency, previous antimicrobial therapy or hospitalization, recurrent UTI and non-urological invasive procedure.

• This nomogram model had a discriminative accuracy of 0.79 (95% CI 0.77–0.82). In the validation cohort, the discriminative accuracy of the model was 0.81 (95% CI 0.77–0.85).

 Johansson et al. [29], 2018

Sweden

Development and validation pre-hospital decision support system (DSS)

S: Pre-hospital ED

i) Development: n = 1921/6323 electronic patient records of adults > 18 yrs. acute infection reviewed; ii) peer review of preliminary DST n = 3 clinical experts iii)

Evaluation and validation of the DST (theoretical test) 12 cases and 250 nurses, iv) Validation of pre-hospital DSS in prospective pilot study

n = 72 patients

Pre-hospital DST

Paper based form

Severe respiratory infection, severe central nervous system infection (CNS), and sepsis

• Severe respiratory infection: confusion, respiratory rate ≥ 30/min, SBP < 90 mmHg, sat. O2 < 90%.

• CNS infection: fever/chills, and one of: confusion, headache, neck stiffness/back pain, petechiae.

• Sepsis: fever + chills, and one of: respiratory rate ≥ 30/min, SBP < 90 mmHg, sat. O2 < 90%.

• All required a previous clinical suspicion.

• Validation cohort: the positive predictive value was 94% (32/34 cases) and for 30 of the 34 patients (88%).

 Siaw-Sakyi [30] 2017

UK

Development: consensus event: comprising audit & expert panel

Validation: Audit pre and post use of the WIRE tool

S: Community

Development: i) Audit of 1500 patient ii) Expert panel of tissue viability nurses: series of group meetings,

Validation: n = 55 patients, 48 wounds. Analysis based on 150 WIRE scores and 47 swab results

Wound Infection Risk- Assessment and Evaluation tool (WIRE)

Checklist

Wound infection

• Pain; slough/necrotic tissues; friable/unhealthy granulation, bed colour; exposure of underlying organs; pocketing/ tunnelling/bridging; non-healing/wound breakdown; maceration/ excoriation; erythema; localised heat; swelling/oedema; crepitus; wound size and depth; type of wound; exudate; diabetes; immunosuppression/ cytotoxic/chemotherapy; cardiac/circulatory; malnutrition; smoking; non-steroidal anti-inflammatory drugs; steroids; multiple antibiotic therapy; lack of concordance, multiple hospital admissions; recurrent wound infections; environmental factors; temperature; pulse rate; blood pressure; respiratory rate, altered mental ability; rigors; nausea/vomiting; and lymphangitis

• 117/150 (78%) cases were matched between the swab & WIRE result confirming presence of infection.

 Tingström et al. [19], 2015

Sweden

Development & validation

S: NHs (n = 6)

n = 204 nursing home residents over 1 year

Development: multi-stage 2006–2014 resulting in 13 item tool;

388 infection events

Clinical decision-making process. Early Detection of Infection Scale (EDIS) instrument [41].

Algorithm

All type of infections

• Items of EDIS: discomfort, unrestrained, aggressiveness, restlessness, confusion, infirm, decreased eating, pain, general signs and symptoms of illness (for example fever, shaking, etc.), Respiratory symptoms, UTI symptoms, Wound infection symptoms and abnormal breath per minute.

• Content validity analysis: 12/13 of the items correlated significantly with at least one other statement.

• Construct validity: “temperature”, “respiratory symptoms” and “general signs and symptoms of illness” were significantly related to “infection”. These last items predicted correct alternative responses in 61% of the cases.

 Afonso et al. [31], 2012

USA and Switzerland

Development & validation clinical decision rule

S: USA: hospital (n = 258), ED & Switzerland: primary care (n = 201)

Secondary analysis of two combined existing data sets

Development set n = 322 patients (70%)

Validation set n = 137 patients (30%)

Decision tree for the diagnosis of influenza

Classification and regression tree

Influenza

• Of the three models: regression reliability and validated, model 2 presented best results and classified two-thirds of patients as low or high risk and had an area under the receiver-operating characteristics curve (AUROCC) of 0.76.

• Patient with suspected influenza have > 37 °C: high risk of flu (58%). And if they do not have fever, but do have chills and/or sweating, flu risk was 18%.

 Chumbler et al. [32], 2010

USA

Development and validation of clinical prediction rule

S: Hospitals (n = 5)

Secondary analysis retrospective cohort study (total n = 1363)

Development set n = 925 patients (70%). Validation group n = 438 patients (30%)

Post-stroke pneumonia prediction system

Logistic regression model

Post-stroke pneumonia

• Abnormal swallowing & history of pneumonia (4 points); followed by greater NIHSS score (3 points); patient being ‘found down’ at symptom onset (3 points); and age > 70 years (2 points).

• The discriminatory accuracy of the 3-level clinical prediction rule denoted low, medium and high risks of pneumonia. This exceeded the acceptable range in both the development group (c statistic: 0.78) and validation group (c statistic: 0.76).

Tool validation

 Gräff et al. [33], 2017

Germany

Retrospective observational study

S: ED

n = 20,836 patients > 16 years

Manchester Triage System (MTS) Adaptation

A computer algorithm

Sepsis

• Breathlessness; heart rate: > 120; Temperature (°C): < 35 or > 41 (orange)/ > 38.5 (yellow); Blood pressure: only mention for pregnancy.

• MTS triage categories of ‘yellow’, ‘orange’ or ‘red’. MTS category ‘green’ or ‘blue’ was judged to be inadequate prioritisations. Patients with severe sepsis with circulation dysfunction were considered adequately categorised only when allocated to ‘orange’ or ‘red’.

• Patients with severe sepsis were appropriately prioritised with a sensitivity of 84.5% (95% CI 78.1 to 89.4), and LR– was 0.330 (95% CI 0.243 to 0.450). In the group with severe sepsis and circulation dysfunction, sensitivity was 61.5% (95% CI 39.3 to 79.8), and LR– was 0.466 (95% CI 0.286 to 0.757).

 Walchok et al. [34], 2017

USA

Retrospective case series review

S: Mixed rural and suburban community

946/1154 patients with sepsis alert and blood culture

Pre-hospital Sepsis Assessment Tool (Pre-SAT) [42]

Paper form

Sepsis

• SIRS criteria [43] + mechanical ventilation, and/or signs of poor perfusion (systolic blood pressure < 90 mmHg).

• 2 signs of SIRS and a known or suspected source of infection required the paramedic to issue a ‘Sepsis Alert’ to the receiving ED. These criteria were used after gaining consensus from the two receiving hospital medical sepsis committees.

• 848/1154 confirmed overall sepsis diagnosis: Positive blood culture 179/946 (18.9%). Antibiotics administered in 72/100 patients

 Jessen et al. [35], 2016

Denmark

Retrospective matched cohort study

S: ED

n = 420

Bacteremia (n = 105)

Non-bacteremia (n = 315)

Clinical decision rule to predict Bacteremia in the ED [44]

Clinical decision rule

Bacteremia

• Suspected endocarditis (3 points); temperature > 39.4 °C (103.0 °F) (3 points); indwelling vascular catheter (2 points); and minor criteria (1 point each): age > 65 years, temperature 38.3–39.3 °C, chills, vomiting, hypotension (SBP < 90 mmHg), white blood cell count > 18,000 cells ×  109/l, bands > 5%, platelets< 150,000 cells ×  109/l and creatinine> 177 μmol/l (2.0 mg/dl).

• The sensitivity of the prediction rule was 94% (95% CI, 88–98%) and the specificity was 48% (95% CI, 42–53%). The AUROCC was 0.83.

Tool testing

 Pasay et al. [36], 2019

Canada

Cluster RCT of

S: Rural NHs (n = 42), n = 21 allocated to each group

n = 1001 residents

The UTI in LTC Facilities Checklist

A clinical decision-making tool without laboratory test

UTI

• No indwelling catheter: Acute dysuria or Temp > 38 °C or 1.1°above baseline on 2 consecutive occasions (4–6 hr. apart). Plus: increased urinary frequency, urgency, incontinence, flank or suprapubic pain or tenderness, and haematuria.

• Indwelling catheter: No other cause of infection and ≥ 1 of: Temp > 38 °C or 1.1°above baseline on 2 consecutive occasions (4–6 hr. apart), flank or suprapubic pain or tenderness, rigors and delirium.

• UTI symptoms were charted in 16% of cases and that urine culture testing occurred in 64.5% of cases (regression coefficient, p = 0.02; 95% CI, 0.001–0.03). Significantly decreased the rate of urine culture testing and antimicrobial prescriptions for UTI (p < 0.001), with no increase in hospital admissions or mortality.

 Amland & Hahn-Cover [37], 2016

USA

Retrospective cohort study

S: Medical centres

(a level 1 trauma centre, a level 2 trauma centre, a women’s and children’s hospital, and 2 community hospitals).

n = 6200 patients aged over 65 years

Clinical decision support system (CDS) based on SIRS [43]

Cloud-based computerized system

Sepsis

• ≥3 of the following 5 criteria were satisfied: (1) temperature > 38.3 °C or < 36 °C; (2) heart rate > 95 beats/min; (3) respiratory rate > 22 breaths/min; (4) white blood cell count > 12,000 cells/mm3 or < 4000 cells/mm3, or > 10% immature (band) forms; or (5) glucose 141 to < 200 mg/dL.

• ≥2 criteria were present and ≥ 1 of the following 4 organ system dysfunction criteria were satisfied: (1) cardiovascular system, SBP < 90 mmHg and/or mean arterial pressure < 65 mmHg; (2) tissue perfusion, serum lactate > 2.0 mmol/L; (3) hepatic system, total bilirubin ≥2.0 mg/ dL and < 10.0 mg/dL; and (4) renal system, serum creatinine ↑0.5 mg/dL from baseline.

• 83% sensitivity and 92% specificity.

 McMaughan et al. [38], 2016

USA

RCT

n = NHs (n = 12)

699 prescriptions for suspected UTI for n = 547 NH residents

Decision-making aid for suspected UTI.

Paper form

UTI

• Acute dysuria; fever (> 37.9 °C) or 1.5 °C increase above baseline temperature; new or worsening urgency, frequency, or incontinence, suprapubic pain; gross haematuria; costovertebral angle (flank) tenderness; rigors, and delirium (recent and abrupt change in mental status).

• The odds of a prescription decreased significantly in homes that succeeded in implementing the decision-making aid (OR = 0.35, 95% CI = 0.16–0.76), compared to homes with no fidelity.

 Umberger et al. [39], 2016

USA

Secondary analysis of a retrospective case series review

n = Hospital, ICU

n = 77 patients with sepsis

Candida Score [45, 46]

Paper form

Candidemia

• Severe sepsis (2 points), surgery at baseline (1 point), total parenteral nutrition (1 point), and Candida colonization (1 point).

• Infection detection with score ≥ 3 points.

• Sensitivity was 50%, specificity was 68.1%, positive predictive value was 15.4%, and negative predictive value was 92.2%.